Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase

Predicting Resistance to Small Molecule Kinase Inhibitors

Drug resistance is a critical challenge in treating diseases like cancer and infectious disease. This study presents a novel computational workflow for predicting on-target resistance mutations to small molecule inhibitors (SMIs). The approach integrates genetic models with alchemical free energy perturbation (FEP+) calculations to identify likely resistance mutations. Specifically, a genetic model, RECODE, leverages cancer-specific mutation patterns to prioritize probable amino acid changes. Physics-based calculations assess the impact of these mutations on protein stability, endogenous substrate binding, and inhibitor binding. We apply this approach retrospectively to gefitinib and osimertinib, two clinical epidermal growth factor receptor (EGFR) inhibitors used to treat nonsmall cell lung cancer (NSCLC). Among hundreds of possible mutations, the pipeline accurately predicted 4 out of 11 and 7 out of 19 known binding site mutations for gefitinib and osimertinib, respectively, including the clinically relevant T790M and C797S resistance mutations. This study demonstrates the potential of integrating genetic models and physics-based calculations to predict SMI resistance mutations. This approach can be applied to other kinases and target classes, potentially enabling the design of next-generation inhibitors with improved durability of response in patients.

Assessment of an Electrostatic Energy-Based Charge Model for Modeling the Electrostatic Interactions in Water Solvent

The protein force field based on the restrained electrostatic potential (RESP) charges has limitations in accurately describing hydrogen bonding interactions in proteins. To address this issue, we propose an alternative approach called the electrostatic energy-based charges (EEC) model, which shows improved performance in describing electrostatic interactions (EIs) of hydrogen bonds in proteins. In this study, we further investigate the performance of the EEC model in modeling EIs in water solvent. Our findings demonstrate that the fixed EEC model can effectively reproduce the quantum mechanics/molecular mechanics (QM/MM)-calculated EIs between a water molecule and various water solvent environments. However, to achieve the same level of computational accuracy, the electrostatic potential (ESP) charge model needs to fluctuate according to the electrostatic environment. Our analysis indicates that the requirement for charge adjustments depends on the specific mathematical and physical representation of EIs as a function of the environment for deriving charges. By comparing with widely used empirical water models calibrated to reproduce experimental properties, we confirm that the performance of the EEC model in reproducing QM/MM EIs is similar to that of general purpose TIP4P-like water models such as TIP4P-Ew and TIP4P/2005. When comparing the computed 10,000 distinct EI values within the range of -40 to 0 kcal/mol with the QM/MM results calculated at the MP2/aug-cc-pVQZ/TIP3P level, we noticed that the mean unsigned error (MUE) for the EEC model is merely 0.487 kcal/mol, which is remarkably similar to the MUE values of the TIP4P-Ew (0.63 kcal/mol) and TIP4P/2005 (0.579 kcal/mol) models. However, both the RESP method and the TIP3P model exhibit a tendency to overestimate the EIs, as evidenced by their higher MUE values of 1.761 and 1.293 kcal/mol, respectively. EEC-based molecular dynamics simulations have demonstrated that, when combined with appropriate van der Waals parameters, the EEC model can closely reproduce oxygen-oxygen radial distribution function and density of water, showing a remarkable similarity to the well-established TIP4P-like empirical water models. Our results demonstrate that the EEC model has the potential to build force fields with comparable accuracy to more sophisticated empirical TIP4P-like water models.

General Charge Transfer Dipole Model for AMOEBA-Like Force Fields

The development of highly accurate force fields is always an importance aspect in molecular modeling. In this work, we introduce a general damping-based charge transfer dipole (D-CTD) model to describe the charge transfer energy and the corresponding charge flow for H, C, N, O, P, S, F, Cl, and Br elements in common bio-organic systems. Then, two effective schemes to evaluate the charge flow from the corresponding induced dipole moment between the interacting molecules were also proposed and discussed. The potential applicability of the D-CTD model in ion-containing systems was also demonstrated in a series of ion-water complexes including Li⁺, Na⁺, K⁺, Mg²⁺, Ca²⁺, Fe²⁺, Zn²⁺, Pt²⁺, F^-, Cl^-, Br^-, and I^- ions. In general, the D-CTD model demonstrated good accuracy and good transferability in both charge transfer energy and the corresponding charge flow for a wide range of model systems. By distinguishing the intermolecular charge redistribution (charge transfer) under the influence of an external electric field from the accompanying intramolecular charge redistribution (polarization), the D-CTD model is theoretically consistent with current induced dipole-based polarizable dipole models and hence can be easily implemented and parameterized. Along with our previous work in charge penetration-corrected electrostatics, a bottom-up approach constructed water model was also proposed and demonstrated. The structure-maker and structure-breaker roles of cations and anions were also correctly reproduced using Na⁺, K⁺, Cl^-, and I^- ions in the new water model, respectively. This work demonstrates a cost-effective approach to describe the charge transfer phenomena. The water and ion models also show the feasibility of a modulated development approach for future force fields.

Systematic Improvement of the Performance of Machine Learning Scoring Functions by Incorporating Features of Protein-Bound Water Molecules

Water molecules at the ligand-protein interfaces play crucial roles in the binding of the ligands, but the behavior of protein-bound water is largely ignored in many currently used machine learning (ML)-based scoring functions (SFs). In an attempt to improve the prediction performance of existing ML-based SFs, we estimated the water distribution with a HydraMap (HM) method and then incorporated the features extracted from protein-bound waters obtained in this way into three ML-based SFs: RF-Score, ECIF, and PLEC. It was found that a combination of HM-based features can consistently improve the performance of all three SFs, including their scoring, ranking, and docking power. HydraMap-based features show consistently good performance with both crystal structures and docked structures, demonstrating their robustness for SFs. Overall, HM-based features, which are a statistical representation of hydration sites at protein-ligand interfaces, are expected to improve the prediction performance for diverse SFs.